Wang et al. BMC Genomics 2010, 11:163 http://www.biomedcentral.com/1471-2164/11/163
METHODOLOGY ARTICLE
Open Access
Improvement of tissue preparation for laser capture microdissection: application for cell type-specific miRNA expression profiling in colorectal tumors
Shuyang Wang1, Lei Wang1, Tengfang Zhu1, Xue Gao4, Jian Li2, Ying Wu1,2*, Hongguang Zhu1,3,4*
Abstract
Background: Laser capture microdissection (LCM) has successfully isolated pure cell populations from tissue sections and the combination of LCM with standard genomic and proteomic methods has revolutionized molecular analysis of complex tissue. However, the quantity and quality of material recovered after LCM is often still limited for analysis by using whole genomic and proteomic approaches. To procure high quality and quantity of RNA after LCM, we optimized the procedures on tissue preparations and applied the approach for cell typespecific miRNA expression profiling in colorectal tumors. Results: We found that the ethanol fixation of tissue sections for 2 hours had the maximum improvement of RNA quality (1.8 fold, p = 0.0014) and quantity (1.5 fold, p = 0.066). Overall, the quality (RNA integrity number, RIN) for the microdissected colorectal tissues was 5.2 ± 1.5 (average ± SD) for normal (n = 43), 5.7 ± 1.1 for adenomas (n = 14) and 7.2 ± 1.2 for carcinomas (n = 44). We then compared miRNA expression profiles of 18 colorectal tissues (6 normal, 6 adenomas and 6 carcinomas) between LCM selected epithelial cells versus stromal cells using Agilent miRNA microarrays. We identified 51 differentially expressed miRNAs (p <= 0.001) between these two cell types. We found that the miRNAs in the epithelial cells could differentiate adenomas from normal and carcinomas. However, the miRNAs in the stromal and mixed cells could not separate adenomas from normal tissues. Finally, we applied quantitative RT-PCR to cross-verify the expression patterns of 7 different miRNAs using 8 LCM-selected epithelial cells and found the excellent correlation of the fold changes between the two platforms (R = 0.996). Conclusions: Our study demonstrates the feasibility and potential power of discovering cell type-specific miRNA biomarkers in complex tissue using combination of LCM with genome-wide miRNA analysis.
Background Molecular profiling of clinical tissue specimens is frequently complicated by their cellular heterogeneity. Laser capture microdissection (LCM) has successfully been used to tackle this problem by isolating pure cell populations from tissue sections [1-3] and the combination of LCM with standard genomic and proteomic methods has revolutionized molecular analysis of complex tissue. It has allowed for the discrimination of
* Correspondence: yingwuholland@yahoo.co.uk; Hongguang_701@shmu.edu. cn Contributed equally 1 Department of Pathology, Shanghai Medical College, Fudan University, 138 Yi Xue Yuan Road, Shanghai 200032, PR China
genomic changes, differential expressions and subsequent signaling effects for a variety of proteins in diagnostic tissues [4-10]. Despite these advances, the quantity and quality of material recovered after LCM is often still limited for analysis by using whole genomic and proteomic approaches [2,11]. MicroRNAs (miRNAs) play important regulatory roles in various cellular pathways including development, cell proliferation, differentiation and apoptosis [12-14]. Demonstrated abnormal expression patterns of miRNAs in human disease tissues highlight their potential use as diagnostic and prognostic biomarkers, especially in the case of cancer [15-20]. In fact, miRNAs have already been demonstrated to function as both tumor
2010 Wang et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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suppressors and oncogenes [21,22]. Furthermore, miRNAs have advantages over mRNAs as cancer biomarkers, since they are very stable in vitro [15] and longlived in vivo [23]. So far, the large majority of published miRNA expression studies utilized whole tumor tissues without separating the truly transformed cancerous cells from those other cell types commonly present within a tumor (e.g. immune, stroma cells and new vasculature, etc). Analysis of such complex tissues could conceal the specific signature of the particular cell type of interest. A potentially powerful method to develop diagnostic tests would be to correlate cell type-specific miRNA profiles with pathologic and clinical outcomes. Combination of LCM and whole genome analysis is an ideal method for cell type-specific expression profiling in complex tissue, however, such a combination has not been widely applied to discover miRNA biomarkers in solid tumors. To explore the possibility of using LCM for genome-wide miRNA analysis, we optimized the procedures on tissue preparation and then compared the miRNA expression profiles of 18 colorectal tissues in LCM selected epithelial cells and stromal cells using Agilent miRNA microarrays. We then applied quantitative RT-PCR to cross-verify the expression patterns of 7 different miRNAs using 8 LCM-selected epithelial cells. In this study, we demonstrate a significant improvement in RNA quality and quantity by prolonged ethanol fixation of tissue sections. We further present 51 significantly differentially expressed miRNAs between the epithelial and stromal cells from colorectal tissues. We then show that the miRNAs in the epithelial cells could differentiate adenomas from normal and carcinomas, however, the miRNAs in the stromal and mixed cells could not separate adenomas from normal tissues. We finally illustrate the correlation of the fold changes between the microarray and quantitative RT-PCR. To our knowledge, this work is the first demonstration of the feasibility and potential power of using a combination of LCM with genome-wide miRNA analysis on discovering cell type-specific miRNA biomarkers in complex tissue.
with p = 0.006, Figure 1B). The maximum improvement of the quality (1.81 fold, p = 0.0014) and quantity (1.52 fold, p = 0.066) were observed in storing the sections with 100% ethanol at -80°C for 2 hours (Figures 1C and 1D).
Effect of RNase inhibitor on RNA quality and quantity
Besides the fixation, we evaluated the effect of RNase inhibitor treatment on the tissue preparation. RNA quality and quantity of the tissue sections in presence and absence of an RNase inhibitor are shown in Additional file 1. The presence of the RNase inhibitor reduced both RNA quality and quantity of one sample (S6), whilst slightly improved the RNA quantity in two samples (S1 and S5). Essentially, there was no considerable improvement in both quality and quantity of RNA recovered from the tissue sections with the inhibitor treatment.
Effect of LCM on RNA quality
Using the improved protocol for tissue preparation, we first determined RNA quality in the hematoxylin-stained sections with and without LCM. All the samples were subjected to the same fixation and staining processes but the only difference was the use of microdissection. The RIN score was 7.6 ± 0.8 (average ± SD) for the sections without LCM and 5.8 ± 1.4 (average ± SD) for the sections with LCM (Figure 2A). Compared to the sections without LCM, the RIN score was decreased by 30% during the microdissection (p = 0.004). We then examined the consistency of RNA quality for the LCM selected epithelial cells derived from 101 colorectal tissues (43 normal, 14 adenomas and 44 carcinomas). On average, the RIN score was 5.2 ± 1.5 (average ± SD) for normal, 5.7 ± 1.1 (average ± SD) for adenoma and 7.2 ± 1.2 (average ± SD) for carcinoma tissues (Figure 2B).
Reliability of LCM and miRNA analysis
Results
Effect of ethanol fixation on RNA quality and quantity
To assess the effect of ethanol fixation on RNA quality and quantity, we immediately fixed fresh tissue sections with 100% ethanol for 10 minutes, and then stored the slides at -80°C for 2, 5 and 24 hours. The experimental conditions and their corresponding RIN scores are shown in Table 1. RNA quality and quantity of these sections in presence and absence of ethanol fixation are displayed in Figure 1. Overall, the ethanol fixation significantly improved RNA quality (1.6 fold with p = 2.86E-10, Figure 1A) and quantity (1.2 fold
Replicate experiments were performed to determine the reliability of combining LCM with genome-wide miRNA analysis. The epithelial cells were microdissected on 61 individual colorectal tissues including 24 normal, 13 tubular adenomas and 24 Dukes' C carcinomas. We performed array hybridizations on these epithelial cells and determined the correlation amongst individual samples derived from the same tissue type (Figures 3A, 3B and 3C). The average correlation (R) of epithelial cells isolated from normal tissues, tubular adenomas and Dukes' C carcinomas was 0.942, 0.963 and 0.937, respectively. To determine the variability of the LCM protocol, we performed triplicate LCM experiments on the same tumor tissue and hybridized the LCM-selected epithelial cells on three individual microarrays. As shown in Figure 3D, the correlation (R) amongst the triplicate experiments was 0.999.
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Table 1 Experimental conditions for tissue preparations
Experiment A-control A B-control B C-control C D-control D E-control E LCM (n = 22) F-control F 11 11 10 min 10 min 2h 2h no no 1 min 1 min no yes 7.6 ± 0.8 5.8 ± 1.4 Section 6 6 6 6 6 6 6 6 6 6 Ethanol fixation no 10 min no 10 min no 10 min no 10 min no no Storage at -80°C no no 2h 2h 5h 5h 24 h 24 h no no Inhibitor* no no no no no no no no no 5 min Staining no no no no no no no no no no LCM no no no no no no no no no no RIN & SD 2.7 ± 0.5 4.8 ± 1.6 3.4 ± 0.8 6.1 ± 1.9 3.1 ± 1.0 4.9 ± 2.1 2.9 ± 0.7 4.4 ± 1.6 3.4 ± 1.9 2.8 ± 0.9 Ethanol fixation (n = 48)
Rnase inhibitor (n = 12)
*Add RNase inhibitor to hemotoxylin solution.
Figure 1 Effect of ethanol fixation on RNA quality and quantity. A) RNA quality (RIN scores) of the tissue sections in the presence (n = 24) and absence (n = 24) of ethanol fixation; B) RNA quantity (ng) of the tissue sections in the presence (n = 24) and absence (n = 24) of ethanol fixation; C) RIN scores of the tissue sections over four time points in the presence (n = 6 per time point) and absence (n = 6 per time point) of ethanol fixation and D) RNA quantity (ng) of the tissue sections over four time points in the presence (n = 6 per time point) and absence (n = 6 per time point) of ethanol fixation. Error bars indicate the corresponding SD. The large errors of the experiments were due to the fact that each tested group consisted of three different tissue types (normal, adenoma and carcinoma) which had the different RNA quality and quantity.
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A
RNA quality (RIN)
10
without LCM
8
with LCM
6
4
2
0
Normal(n=4)
Adenoma(n=3)
Carcinoma (n=4)
B
RNA quality (RIN)
10
with LCM
8
6
4
2
0
Normal(n=43)
Adenoma (n=14)
Carcinoma (n=44)
Figure 2 Effect of LCM on RNA quality. A) RNA quality (RIN scores) of the hematoxylin-stained sections with (n = 11) and without (n = 11) LCM and B) RNA quality (RIN scores) of the LCM selected epithelial cells derived from 43 normal, 14 adenoma and 44 carcinoma tissues. Error bars indicate the corresponding SD.
Cell type-specific miRNA expression profiles
Using Agilent miRNA microarrays containing 723 human miRNA probe sets, we profiled the miRNA expression of 18 colorectal tissues in LCM selected epithelial and stromal cells. Significance analysis resulted in the identification of 51 miRNAs as differentially expressed between the epithelial and stromal cell types (Table 2). Figure 4A illustrates an unsupervised
hierarchical clustering of these differentially expressed miRNAs and shows that the clustering placed 18/18 epithelial cells in one group and 18/18 stromal cells in another group. Expression levels of 723 human miRNAs in the epithelial and stromal cells of colorectal tissues are given in Additional file 2. We then assessed the miRNA expression profiles of the colorectal tumors in the epithelial cells and
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A
1.00 0.90 0.80
B
1.00 0.90
Correlation (R)
0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00
Correlation (R)
0.80 0.70 0.60
0.50
0.40 0.30 0.20 0.10
62Cl 21DL 13CL 14DL 63Cl
55AL 59AL 61AL 62AL 63AL 64AL 65AL 66AL 67AL 73AL 75AL 72AL 76AL 77AL 78AL 81AL 82AL 83AL 84AL 86AL 87AL 88AL 89AL 90AL
LCM-selected normal epithelial cells
LCM-selected epithelial cells of tubular adenoma
C
1.00
D
8000 14BL-1, R=0.9992 14BL-2, R=0.9994 14BL-3, R=0.9991 6000
0.90
0.80
Correlation (R)
0.70
0.50 0.40 0.30 0.20 0.10 0.00
Average signal
0.60
4000
2000
6BL
14BL
21BL
39BL
72BL
10BL
12BL
26BL
37BL
48BL
40BL
36BL
52BL
73BL
34BL
38BL
76BL
4BL
5BL
25BL
46BL
63BL
68BL
82BL
0 0 2000 4000 6000 8000
LCM-selected epithelial cells of Dukes'C carcinoma
Signal of individual sample
Figure 3 Reliability of LCM and miRNA analysis. A) correlation amongst individual samples of epithelial cells derived from 24 normal colorectal tissues; B) correlation amongst individual samples of epithelial cells derived from 13 colorectal tubular adenomas; C) correlation amongst individual samples of epithelial cells derived from 24 colorectal Dukes' C carcinomas and D) correlation amongst triplicate LCM experiments.
identified 26 miRNAs that could differentiate adenomas from normal and carcinoma tissues (Figure 4B, Additional file 3). We further evaluated the miRNA profiles of the colorectal tumors in the stromal cells and identified 21 differentially expressed miRNAs that separated normal-adenomas into one group and carcinomas into another group (Figure 4C, Additional file 4). We finally examined the miRNA profiles of the colorectal tumors in the mixed cell types (epithelial and stromal cells) and identified 46 differentially expressed miRNAs amongst normal, adenoma and carcinoma tissues (Figure 4D, Additional file 5). The similar cases were observed in both stromal and mixed cell types where the miRNAs could not separate adenomas from normal tissues. We compared the expression profiles of 5 miRNAs in colorectal tumors with data previously published [24]. Schetter et al. used whole colorectal tissues while our study used LCM selected epithelial cells. Using the whole colorectal tissues, significant fold changes were identified in only one miRNA for adenomas and 5 miRNAs for carcinomas, while considerable changes were
seen in 3 miRNAs for adenomas and 4 miRNAs for carcinomas when we used the pure epithelial cells (Table 3). The overall fold-changes obtained on the whole colorectal tissues were considerably lower than those determined using the pure epithelial cells.
Across-platform comparison
To examine consistency with other platform, data from quantitative RT-PCR were generated on 7 miRNAs using 8 LCM-selected epithelial cells derived from 4 pairs of colorectal tumor tissues. The correlation (R) of fold changes between Agilent miRNA microarrays and quantitative RT-PCR was 0.996. The expression patterns of the miRNAs for 4 pairs of the colorectal tumors are shown in Figure 5. The results demonstrate that the miRNA signatures discovered using Agilent miRNA microarrays are highly reliable.
Discussion Combination of LCM with genome-wide miRNA analysis has not been widely applied to discover miRNA
70CL
85CL
49DL
12CL
24DL
42DL
67CL
4CL
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Table 2 Differentially expressed miRNAs in epithelial and stromal cells of colorectal tissues
Name hsa-miR-143 hsa-miR-145 hsa-miR-133a hsa-miR-139-5p hsa-miR-125b hsa-miR-149 hsa-let-7f-1* hsa-miR-143* hsa-miR-30a hsa-miR-214 hsa-miR-199a-5p hsa-miR-195 hsa-miR-365 hsa-miR-136 hsa-miR-129-3p hsa-miR-30a* hsa-miR-497 hsa-miR-140-5p hsa-miR-877* hsa-miR-199b-3p hsa-miR-22 hsa-miR-490-3p hsa-miR-23b hsa-miR-140-3p hsa-miR-141* hsa-miR-7-1* hsa-miR-194* hsa-miR-760 hsa-miR-513c hsa-miR-200a* hsa-miR-148a hsa-miR-501-5p hsa-miR-601 hsa-miR-7 hsa-miR-500 hsa-miR-210 hsa-miR-892b hsa-miR-200b* hsa-miR-196a hsa-miR-192 hsa-miR-192* hsa-miR-96 hsa-miR-203 hsa-miR-215 hsa-miR-375 hsa-miR-194 Geomean Epithelial 64 512 2 2 256 8 1 1 32 64 128 128 128 2 2 1 64 32 8 512 512 1 1024 64 2 4 4 8 4 8 512 8 16 64 16 128 16 32 64 2048 64 64 128 1024 128 2048 Stromal 512 4096 16 8 1024 32 8 8 128 256 512 512 512 8 8 4 256 128 16 1024 1024 2 2048 128 1 2 1 2 1 2 128 2 2 8 2 16 2 2 2 128 2 2 2 32 4 16 Fold change Epithelial/stromal 0.1 0.1 0.1 0.3 0.3 0.3 0.1 0.1 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.5 0.5 2 2 4 4 4 4 4 4 8 8 8 8 8 16 32 16 32 32 64 32 32 128 Unpaired t-test p-value 1.30E-06 5.60E-06 2.40E-07 2.20E-05 2.60E-07 8.30E-09 6.80E-06 1.80E-06 5.30E-07 2.80E-06 6.20E-07 3.10E-06 3.60E-07 1.10E-05 6.60E-05 4.00E-05 2.30E-05 3.70E-07 2.10E-05 7.70E-07 9.40E-06 8.90E-05 4.10E-06 4.50E-05 6.90E-05 6.20E-05 2.90E-06 7.20E-05 3.80E-05 1.70E-06 1.30E-06 4.10E-07 5.60E-05 5.00E-06 5.10E-07 1.30E-05 4.10E-09 1.50E-08 5.90E-07 1.60E-07 1.40E-10 9.60E-06 2.70E-08 1.20E-06 1.00E-05 1.20E-06 Stromal Stromal Stromal Stromal Stromal Stromal Stromal Stromal Stromal Stromal Stromal Stromal Stromal Stromal Stromal Stromal Stromal Stromal Stromal Stromal Stromal Stromal Stromal Stromal Epithelial Epithelial Epithelial Epithelial Epithelial Epithelial Epithelial Epithelial Epithelial Epithelial Epithelial Epithelial Epithelial Epithelial Epithelial Epithelial Epithelial Epithelial Epithelial Epithelial Epithelial Epithelial Cell type
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Table 2: Differentially expressed miRNAs in epithelial and stromal cells of colorectal tissues (Continued)
hsa-miR-429 hsa-miR-200b hsa-miR-141 hsa-miR-200a hsa-miR-200c 512 2048 1024 1024 1024 4 32 8 8 8 128 64 128 128 128 3.80E-08 1.60E-05 6.60E-07 7.90E-07 1.10E-05 Epithelial Epithelial Epithelial Epithelial Epithelial
biomarkers in solid tumors. This is due to the facts that the tiny amounts of miRNA present in the cells (~0.001-0.1% of total RNA) and RNA recovered from LCM is typically very poor in both quality and quantity using conventional LCM procedures [25]. RNA degradation is primarily due to endogenous RNases that are activated in an aqueous environment. Based on this observation, we tested ethanol fixation and RNase
inhibitor treatment on tissue preparations to procure high-quality yields of RNA. We used ethanol fixation to minimize the tissue sections for exposure to water, whereas RNase inhibitor treatment was used to inhibit the reactivation of endogenous RNases during the staining process. We found that ethanol fixation of tissue sections is the preferred procedure for ensuring the highest quality and
Figure 4 Cell type-specific miRNA expression profiles. A) hierarchical clustering of 51 miRNA expression profiles in LCM selected epithelial and stromal cells from 18 colorectal tissues (n = 6 normal, n = 6 adenomas and n = 6 carcinomas); B) hierarchical clustering of 26 miRNA expression profiles in LCM selected epithelial cells from the colorectal tissues; C) hierarchical clustering of 21 miRNA expression profiles in LCM selected stromal cells from the colorectal tissues and D) hierarchical clustering of 46 miRNA expression profiles in the mixed cell types (epithelial and stromal cells) from the colorectal tissues. The mean signal from biological replicate samples was used for the clustering. Colored bars indicate the range of normalized log2-based signals.
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Table 3 Comparison of miRNA expression profiles between mixed and epithelial cell types of colorectal tumors
Name Adenoma vs. paired nontumorous tissue hsa-miR-20a hsa-miR-21 hsa-miR-106a hsa-miR-181b hsa-miR-203 Carcinoma vs. paired nontumorous tissue hsa-miR-20a hsa-miR-21 hsa-miR-106a hsa-miR-181b hsa-miR-203
a b
Mixed cell typesa p value 0.82 0.006 0.19 0.27 0.14 < 0.001 < 0.001 < 0.001 < 0.001 adenoma (5.7 ± 1.1) > normal tissue (5.2 ± 1.5). This could be due to the fact that RNase activity in the carcinoma tissue is lower than that in the normal tissue [29]. Recently, Ibberson et al. reported that RNA degradation compromised the reliability of miRNA expression profiling and stated that total RNA degradation with RIN values less than 7 should not be used for analysis of individual miRNAs [30]. In our study, we used a mirVana miRNA Isolation kit to prepare total RNA for the miRNA analysis. The RNA isolation procedure combining the advantages of organic extraction and solid-phase extraction can effectively recover small RNAs. The RNA quality recovered from our LCM and isolation procedures is 37% with RIN ≥ 7, 39% with RIN ≥ 5 and 24% with RIN /= 7-10, good for RIN >/= 5 and poor for RIN < 5. Variation of RNA quality and associated RIN scores are displayed in Additional file 7.
Genome-wide miRNA analysis
Human miRNA microarrays (Agilent Technologies, Santa Clara, CA) were used to compare the expression profiles of 18 colorectal tissues (n = 6 normal, n = 6 adenomas and n = 6 carcinomas) between LCM selected epithelial cells versus stromal cells. Furthermore, the microarray platform was used to determine the reproducibility of the LCM protocol using the pure epithelial cells isolated from 61 colorectal tissues (n = 24 normal, n = 13 tubular adenomas and n = 24 Dukes' C
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carcinomas). The microarray contains probes for 723 human miRNAs from the Sanger database v.10.1. Total RNA (100 ng) derived from each of the colorectal samples were used as inputs for labeling via Cy3 incorporation. Microarray slides were scanned by XDR Scan (PMT100, PMT5). The labeling and hybridization were performed at Shanghai Biochip Company according to the protocols in the Agilent miRNA microarray system.
Quantitative RT-PCR
correlation (R) between individual samples in the same tissue type was determined using the normalized signals.
Additional file 1: Effect of RNase inhibitor on RNA quality and quantity. A) RNA quality (RIN scores) of tissue sections in the presence (n = 6) and absence (n = 6) of RNase inhibitors and B) RNA quantity (ng) of the tissue sections in the presence (n = 6) and absence (n = 6) of RNase inhibitor. S1, S5 and S6 indicates sample 1, 5 and 6 respectively. Error bars represent the corresponding SD. Additional file 2: Expression levels of 723 human miRNAs in LCM selected epithelial and stromal cells from colorectal tissue. The table lists the mean normalized signals, the corresponding SD and fold changes of 723 human miRNAs in LCM selected epithelial and stromal cells of colorectal normal (n = 6), adenoma (n = 6) and carcinoma tissues (n = 6). Additional file 3: Differentially expressed miRNAs in LCM selected epithelial cells from colorectal tissue. The table lists the mean normalized signals, fold changes and ANOVA p-values of 26 differentially expressed miRNAs in LCM selected epithelial cells of colorectal normal (n = 6), adenoma (n = 6) and carcinoma tissues (n = 6). Additional file 4: Differentially expressed miRNAs in LCM selected stromal cells from colorectal tissue. The table lists the mean normalized signals, fold changes and ANOVA p-values of 21 differentially expressed miRNAs in LCM selected stromal cells of colorectal normal (n = 6), adenoma (n = 6) and carcinoma tissues (n = 6). Additional file 5: Differentially expressed miRNAs in the mixed cell types (epithelial and stromal cells) from colorectal tissue. The table lists the mean normalized signals, fold changes and ANOVA p-values of 46 differentially expressed miRNAs in the mixed cell types (epithelial and stromal cells) of colorectal normal (n = 6), adenoma (n = 6) and carcinoma tissues (n = 6). Additional file 6: Schematic depiction of the improved protocol on tissue preparation for laser capture microdissection. The figure shows the procedures of the optimized ethanol-fixation protocol on tissue preparation for LCM. Additional file 7: Variation of RNA quality and its associated RIN score. A) gel electrophoresis patterns of total RNA samples with various RNA quality and B) electropherograms of total RNA samples with associated RIN scores.
Quantitative RT-PCR was performed using Taqman MicroRNA assays (Applied Biosystems, Foster City, CA) according to the manufacturer's instructions with the Light Cycling 480 system (Roche Applied Science, Indianapolis). The assays were performed for 7 miRNAs (hsamiR-143, hsa-miR-145, hsa-miR-195, hsa-miR-375, hsamiR-497, hsa-miR-7 and hsa-miR-96) using 8 LCMselected epithelial cells derived from 4 pairs of colorectal tumor tissues. The expression level of the small nuclear RNA U47 was used as the normalization control. All assays were carried out in triplicate.
Data analysis Significance analysis of different tissue preparations
Paired t-test was performed on RNA quality and quantity data derived from the different tissue preparations. The standard deviation (SD) of the replicate experiments was determined to assess the variability of the tissue preparations.
Differential miRNA expression analysis
The microarray image information was converted into spot intensity values using Scanner Control Software Rev. 7.0 (Agilent Technologies, Santa Clara, CA). The signal after background subtraction was exported directly into the GeneSpring GX10 software (Agilent Technologies, Santa Clara, CA) for quantile normalization. The mean normalized signal from biological replicates was used for comparative expression analysis. Unpaired t-test with Benjamini-Hochberg correction (p value Correlation analysis
Acknowledgements We would like to thank Xianxin Meng for technical support, Shaohua Lu, Yiping Ren and Zhaoyong Li for discussions, and Dennis Merkle and Nevenka Dimitrova for helpful comments. This work was supported by grants for Program from Science and Technology Commission of Shanghai Municipality (06DZ22904) and Company Research (2007-062) from Philips. Author details 1 Department of Pathology, Shanghai Medical College, Fudan University, 138 Yi Xue Yuan Road, Shanghai 200032, PR China. 2Department of Healthcare, Philips Research Asia - Shanghai, No 10, Lane 888, Tian Lin Road, Shanghai 200233, PR China. 3Division of Surgical Pathology, Huashan Hospital, Fudan University, 12 middle-Wulumuqi Road, Shanghai 200040, PR China. 4Research Center for Pathology, Institute of Biomedical Sciences, Fudan University, 138 Yi Xue Yuan Road, Shanghai 200032, PR China. Authors' contributions The study was designed and organised by HZ and YW. The experimental work was carried out by SW, LW, TZ and XG with coordination by HZ and YW. The statistical analysis was performed by JL. The manuscript was initially drafted by SW. YW organized the final versions of the manuscript and arranged submission. All authors read and approved the final manuscript. Received: 1 November 2009 Accepted: 10 March 2010 Published: 10 March 2010
Pearson correlation was performed with all of 723 human miRNAs after the quantile normalization. The
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References 1. Emmert-Buck MR, Bonner RF, Smith PD, Chuaqui RF, Zhuang Z, Goldstein SR, Weiss RA, Liotta LA: Laser capture microdissection. Science 1996, 274:998-1001. 2. Bonner RF, Emmert-Buck M, Cole K, Pohida T, Chuaqui R, Goldstein S, Liotta LA: Laser capture microdissection: molecular analysis of tissue. Science 1997, 278:1481-1483. 3. Fend F, Raffeld M: Laser capture microdissection in pathology. J Clin Pathol 2000, 53:666-672. 4. Chandrasekharappa SC, Guru SC, Manickam P, Olufemi SE, Collins FS, Emmert-Buck MR, Debelenko LV, Zhuang Z, Lubensky IA, Liotta LA, Crabtree JS, Wang Y, Roe BA, Weisemann J, Boguski MS, Agarwal SK, Kester MB, Kim YS, Heppner C, Dong Q, Spiegel AM, Burns AL, Marx SJ: Positional cloning of the gene for multiple endocrine neoplasia-type 1. Science 1997, 276:404-407. 5. Brown MR, Chuaqui R, Vocke CD, Berchuck A, Middleton LP, EmmertBuck MR, Kohn EC: Allelic loss on chromosome arm 8p: analysis of sporadic epithelial ovarian tumors. Gynecol Oncol 1999, 74:98-102. 6. Takeshima Y, Amatya VJ, Daimaru Y, Nakayori F, Nakano T, Inai K: Heterogeneous genetic alterations in ovarian mucinous tumors: application and usefulness of laser capture microdissection. Hum Pathol 2001, 32:1203-1208. 7. Leethanakul C, Patel V, Gillespie J, Shillitoe E, Kellman RM, Ensley JF, Limwongse V, Emmert-Buck MR, Krizman DB, Gutkind JS: Gene expression profiles in squamous cell carcinomas of the oral cavity: use of laser capture microdissection for the construction and analysis of stagespecific cDNA libraries. Oral Oncol 2000, 36:474-483. 8. Chan S, Murray PG, Franklyn JA, McCabe CJ, Kilby MD: The use of laser capture microdissection (LCM) and quantitative polymerase chain reaction to define thyroid hormone receptor expression in human 'term' placenta. Placenta 2004, 25:758-762. 9. Wulfkuhle JD, Aquino JA, Calvert VS, Fishman DA, Coukos G, Liotta LA, Petricoin EF: Signal pathway profiling of ovarian cancer from human tissue specimens using reverse-phase protein microarrays. Proteomics 2003, 3:2085-2090. 10. Liotta LA, Espina V, Mehta AI, Calvert V, Rosenblatt K, Geho D, Munson PJ, Young L, Wulfkuhle J, Petricoin EF: Protein microarrays: meeting analytical challenges for clinical applications. Cancer Cell 2003, 3:317-325. 11. Goldsworth SM, Stockton PS, Trempus CS, Foley JF, Maronpot RR: Effects of fixation on RNA extraction and amplification from laser capture microdissected tissue. Mol Carcinog 1999, 25:86-91. 12. Bartel DP: MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 2004, 116:281-297. 13. Ambros V: The functions of animal microRNAs. Nature 2004, 431:350-355. 14. He L, Hannon GJ: MicroRNAs: small RNAs with a big role in gene regulation. Nat Rev Genet 2004, 5:522-531. 15. Lu J, Getz G, Miska EA, Alvarez-Saavedra E, Lamb J, Peck D, SweetCordero A, Ebert BL, Mak RH, Ferrando AA, Downing JR, Jacks T, Horvitz HR, Golub TR: MicroRNA expression profiles classify human cancers. Nature 2005, 435:834-838. 16. Iorio MV, Ferracin M, Liu CG, Veronese A, Spizzo R, Sabbioni S, Magri E, Pedriali M, Fabbri M, Campiglio M, Ménard S, Palazzo JP, Rosenberg A, Musiani P, Volinia S, Nenci I, Calin GA, Querzoli P, Negrini M, Croce CM: MicroRNA gene expression deregulation in human breast cancer. Cancer Res 2005, 65:7065-7070. 17. Esquela-Kerscher A, Slack FJ: Oncomirs - microRNAs with a role in cancer. Nat Rev Cancer 2006, 6:259-269. 18. Calin GA, Croce CM: MicroRNA signatures in human cancers. Nat Rev Cancer 2006, 6:857-866. 19. Yanaihara N, Caplen N, Bowman E, Seike M, Kumamoto K, Yi M, Stephens RM, Okamoto A, Yokota J, Tanaka T, Calin GA, CG Liu CG, Croce CM, Harris CC: Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell 2006, 9:189-198. 20. Bandrés E, Cubedo E, Agirre X, Malumbres R, Zárate R, Ramirez N, Abajo A, Navarro A, Moreno I, Monzó M, García-Foncillas J: Identification by Realtime PCR of 13 mature microRNAs differentially expressed in colorectal cancer and non-tumoral tissues. Mol Cancer 2006, 5:29. 21. He L, Thomson JM, Hemann MT, Hernando-Monge E, Mu D, Goodson S, Powers S, Cordon-Cardo C, Lowe SW, Hannon GJ: A microRNA polycistron as a potential human oncogene. Nature 2005, 435:828-833.
22. Johnson SM, Grosshans H, Shingara J, Byrom M, Jarvis R, Cheng A, Labourier E, Reinert EKL, Brown D, Slack FJ: RAS is regulated by the let-7 microRNA family. Cell 2005, 120:635-647. 23. Lim LP, Lau NC, Garrett-Engele P, Grimson A, Schelter JM, Castle J, Bartel DP, Linsley PS, Johnson JM: Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature 2005, 433:769-773. 24. Schetter AJ, Leung SY, Sohn JJ, Zanetti KA, Bowman ED, Yanaihara N, Yuen ST, Chan TL, Kwong DL, Au GK, Liu CG, Calin GA, Croce CM, Harris CC: MicroRNA expression profiles associated with prognosis and therapeutic outcome in colon adenocarcinoma. JAMA 2008, 299:425-436. 25. Morrogh M, Olvera N, Bogomolniy F, Borgen PI, King TA: Tissue preparation for laser capture microdissection and RNA extraction from fresh frozen breast tissue. Bio Techniques 2007, 43:41-48. 26. Espina V, Wulfkuhle JD, Calvert VS, VanMeter A, Zhou W, Coukos G, Geho DH, Petricoin EF III, Liotta LA: Laser-capture microdissection. Nature Protocols 2006, 1:586-603. 27. Kube DM, Savci-Heijink CD, Lamblin AF, Kosari F, Vasmatzis G, Cheville JC, Connelly DP, Klee GG: Optimization of laser capture microdissection and RNA amplification for gene expression profiling of prostate cancer. BMC Mol Bio1 2007, 8:25. 28. Zhou H, Diaw L, Dang H, Moore H, Robb J, Vaught J, Compton C, Gillespie J: Efficacy of RNase Inhibitors to preserve RNA in colon cancer tissue sections for laser capture microdissection. BRN Symposium 2009, poster:34. 29. Roth JS: Ribonuclease activity and cancer: A review. Cancer Res 1963, 23:657-666. 30. Ibberson D, Benes V, Muckenthaler MU, Castoldi M: RNA degradation compromises the reliability of microRNA expression profiling. BMC Biotechnology 2009, 9:102. 31. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES: Molecular classification of cancer: class discovery and class prediction by gene expression profiling. Science 1999, 286:531-537. 32. Perou CM, Srlie T, Eisen MB, Rijn van de M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA, Fluge O, Pergamenschikov A, Williams C, Zhu SX, Lnning PE, Brresen-Dale AL, Brown PO, Botstein D: Molecular portraits of human breast tumors. Nature 2000, 406:747-752. 33. Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, Barrette T, Pandey A, Chinnaiyan AM: Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Proc Natl Acad Sci USA 2004, 101:9309-9314. 34. Fearon ER, Vogelstein B: A genetic model for colorectal tumorigenesis. Cell 1990, 61:759-767. 35. Yu P, Rowley DA, Fu YX, Schreiber H: The role of stroma in immune recognition and destruction of well-established solid tumors. Current Opinion in Immunology 2006, 18:226-231. 36. Imbeaud S, Graudens E, Boulanger V, Barlet X, Zaborski P, Eveno E, Mueller O, Schroeder A, Auffray C: Towards standardization of RNA quality assessment using user-independent classifiers of microcapillary electrophoresis traces. Nuc Acids Res 2005, 33:e56. 37. Schroeder A, Mueller O, Stocker S, Salowsky R, Leiber M, Gassmann M, Lightfoot S, Menzel W, Granzow M, Ragg T: The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Mol Biol 2006, 7:3.
doi:10.1186/1471-2164-11-163 Cite this article as: Wang et al.: Improvement of tissue preparation for laser capture microdissection: application for cell type-specific miRNA expression profiling in colorectal tumors. BMC Genomics 2010 11:163.